code: def run_same_policy(args, stop):
      """Use the same policy for both agents (trivial case)."""
      config = (
          PPOConfig()
          .environment("RockPaperScissors")
          .framework(args.framework)
      )
  
      results = tune.Tuner(
          "PPO",
          param_space=config,
          run_config=air.RunConfig(stop=stop, verbose=1),
      ).fit()
  
      if args.as_test:
          # Check vs 0.0 as we are playing a zero-sum game.
          check_learning_achieved(results, 0.0)
  
file_path: ray/rllib/examples/rock_paper_scissors_multiagent.py
imports: import argparse
  import os
  from pettingzoo.classic import rps_v2
  import random
  import ray
  from ray import air, tune
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.ppo import PPO, PPOConfig, PPOTF1Policy, PPOTF2Policy, PPOTorchPolicy
  from ray.rllib.env import PettingZooEnv
  from ray.rllib.examples.policy.rock_paper_scissors_dummies import BeatLastHeuristic, AlwaysSameHeuristic
  from ray.rllib.policy.policy import PolicySpec
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.test_utils import check_learning_achieved
  from ray.tune.registry import register_env
label_desc: Use image node_id 2 for calling a global function with example usage: run_same_policy(args, stop) without return types